We recall that, like [1]}, the minimization problem of (REF ) is equivalent to the simpler problem
than the optimization of existing classic bounds. In fact, it
suffices to minimize an objective function, which is linear
with respect to KL-divergences. This leads to Gibbs posteriors, and might be the reason why the obtained algorithm
reduces the meta-overfitting problem.
